356 Generating consistent Cloud Property Datasets from 3 decades of satellite observations applying an OE Retrieval Algorithm in the ESA Cloud_cci Project

Wednesday, 9 July 2014
Matthias Jerg, Deutscher Wetterdienst, Offenbach, Germany; and S. Stapelberg, A. Kniffka, M. Stengel, R. Hollmann, and C. A. Poulsen

Within the ESA Climate Change Initiative (CCI) Cloud project (Cloud_cci) the synergetic capabilities of past, existing, and upcoming European and American satellite missions are investigated aiming at the composition of long-term coherent cloud property datasets. The synergetic approach allows not only for improved accuracy and extended temporal and spatial sampling of retrieved cloud properties, better than those provided by single instruments alone, but potentially also for improved (inter-)calibration and enhanced homogeneity and stability of the derived time series. Such advances are required to make further progress in the generation of satellite-based climate datasets, which allow more applications and thus will potentially lead to a better understanding of climate.

In this presentation we will discuss the benefit of using an optimal estimation retrieval framework, which provides consistence among the retrieved cloud variables and pixel-based uncertainty estimates. It also enables the homogeneous generation of cloud property datasets composed of different satellites sensors such as AVHRR, MODIS and AATSR. The strengths and weaknesses of the Cloud_cci datasets will be analyzed. Furthermore, we will highlight examples of evaluations and analyses of the three-year spanning prototype dataset that has been processed for AVHRR and MODIS in the project. The data was confronted with other well-established satellite-based datasets and ground observations. It has also been used already in comparisons with regional and global climate models and reanalysis. Finally, we will give an outlook on the three decades spanning, final dataset, which is soon to be generated.

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